题目：Nonparametric Learning Rules from Bandit Experiments: The Eyes Have It!
主讲人：Hu, Yingyao，Assosiate Professor, Johns Hopkins University
How do people learn? We assess, in a distribution-free manner, subjects' learning and choice rules in dynamic two-armed bandit learning experiment. To aid in identi- fication and estimation, we use auxiliary measures of subjects' beliefs, in the form of their eye-movements during the experiment. Our estimated choice probabilities and learning rules have some distinctive features; notably, subjects are more reluctant to 'update down' following unsuccessful choices, than 'update up' following successful choices. The profits from following the estimated learning and decision rules are smaller (by about 25% of typical experimental earnings) than what would be obtained from an optimal Bayesian learning model, but comparable to the profits from alternative non- Bayesian learning models, including reinforcement learning and a simple 'win-stay' choice heuristic.
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